In addition to scene refinement, our method can be used to synthesize novel views on multi-view stereo datasets.
We show in this section that our approach outperforms state-of-the-art neural rendering pipelines that operate on point-cloud input data.
In particular, we provide a qualitative and quantitative evaluation of novel view synthesis on four scenes of the tanks and temples dataset [1]}.
These scenes are Train, Lighthouse, Playground, and M60.
They consist of 300-350 images in Full-HD resolution captured by a high-end video camera.
We use COLMAP [2]} to reconstruct the dense point-cloud as well as the camera extrinsics and intrinsics.
The number of points in the datasets are in order \(10M, 8M, 12M,\)  and \(11M\) .
For evaluation, 5% of the input frames are put aside for testing.
The remaining images are used to train each pipeline.
